On the Creation of a Corpus for Coherence Evaluation of Discursive Units (2020.lrec-1)
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| Challenge: | Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering. |
| Approach: | They propose to use a variety of corruption strategies to build a corpus of incoherent pairs of sentences by swapping their discourse connective or a discourse argument. |
| Outcome: | The proposed corpus is constructed from discourse argument pairs from the Penn Discourse Tree Bank and is compared with existing corpus models. |
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How coherent are neural models of coherence? (2020.coling-main)
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| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
| Approach: | They propose four generic evaluation tasks that capture coherence-specific properties . they aim at capturing correct use of discourse connectives and lexical cohesion . |
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Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models (2021.naacl-main)
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| Challenge: | a common approach to coherence evaluation is shuffling the sentence order of a text, creating incoherent text samples that need to be discriminated from the original. |
| Approach: | They propose an extendable set of test suites addressing different aspects of discourse and dialogue coherence. |
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Jigsaw Pieces of Meaning: Modeling Discourse Coherence with Informed Negative Sample Synthesis (2024.findings-eacl)
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| Challenge: | Existing work on creating “informed” incoherent samples for coherence modeling has focused on permutations of a coherent document . |
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DDisCo: A Discourse Coherence Dataset for Danish (2022.lrec-1)
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| Challenge: | Discourse coherence models have been developed using randomly shuffled texts instead of highly edited and coherent data. |
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Joint Modeling of Entities and Discourse Relations for Coherence Assessment (2025.emnlp-main)
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| Challenge: | Existing work on coherence modeling focuses on entity features or discourse relation features, with little attention given to combining the two. |
| Approach: | They propose two methods for jointly modeling entities and discourse relations for coherence assessment. |
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Coherent or Not? Stressing a Neural Language Model for Discourse Coherence in Multiple Languages (2023.findings-acl)
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| Challenge: | Existing work on coherence assessment using NLMs focuses on properties acquired from stand-alone sentences, but their ability to model discourse and pragmatic phenomena is still unclear. |
| Approach: | They propose to use a Neural Language Model to assess coherence in multiple languages to compare models' performance and to examine their performance in a cross-language scenario. |
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COHESENTIA: A Novel Benchmark of Incremental versus Holistic Assessment of Coherence in Generated Texts (2023.emnlp-main)
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| Challenge: | linguistics has been used to assess the coherence of generated texts . a benchmark of coherency scores is developed to measure the quality of generated text . |
| Approach: | They propose a benchmark to assess coherence of automatically generated texts . they use global and incremental methods to score sentences for coherency . |
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How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation (2022.coling-1)
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| Challenge: | Existing methods to evaluate summary coherence are often evaluated using disparate datasets and metrics. |
| Approach: | They propose to use automatic evaluation to evaluate coherence of summaries by selecting high-scoring candidates. |
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DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence (2023.eacl-main)
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| Challenge: | DiscoScore is a parametrized discourse metric that uses BERT to model discourse coherence . it is weak when operated at system level, and is therefore not reliable in a way to spot improvements . |
| Approach: | They propose a parametrized discourse metric which uses BERT to model discourse coherence from different perspectives. |
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A Novel Computational Modeling Foundation for Automatic Coherence Assessment (2025.naacl-long)
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| Challenge: | Existing models for text coherence assessment rely on a proxy task . however, this approach does not capture the full range of factors contributing to coherency. |
| Approach: | They propose a formal linguistic definition of what makes a discourse coherent and formalize these conditions as respective computational tasks that are jointly trained. |
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